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@Article{Akansu2012,
  Title                    = {Toeplitz approximation to empirical correlation matrix of asset returns: A signal processing perspective},
  Author                   = {Akansu, Ali N and Torun, Mustafa U},
  Journal                  = {Selected Topics in Signal Processing, IEEE Journal of},
  Year                     = {2012},
  Number                   = {4},
  Pages                    = {319--326},
  Volume                   = {6},

  __markedentry            = {[postgres:]},
  File                     = {Akansu2012.pdf:99_static/dl_group/Akansu2012.pdf:PDF},
  Owner                    = {postgres},
  Publisher                = {IEEE},
  Timestamp                = {2016.02.29}
}

@Article{Babacan2012,
  Title                    = {Bayesian Group-Sparse Modeling and Variational Inference},
  Author                   = {Babacan, S Derin and Nakajima, Shinichi and Do, Minh N},
  Journal                  = {IEEE transactions on signal processing},
  Year                     = {2014},
  Number                   = {9-12},
  Pages                    = {2906--2921},
  Volume                   = {62},

  Owner                    = {postgres},
  Publisher                = {Institute of Electrical and Electronics Engineers},
  Timestamp                = {2016.02.29}
}

@Article{Cambareri2015,
  Title                    = {Low-Complexity Multiclass Encryption by Compressed Sensing},
  Author                   = {Cambareri, Valerio and Mangia, Mauro and Pareschi, Fabio and Rovatti, Riccardo and Setti, Gianluca},
  Journal                  = {Signal Processing, IEEE Transactions on},
  Year                     = {2015},
  Number                   = {9},
  Pages                    = {2183--2195},
  Volume                   = {63},

  __markedentry            = {[postgres:]},
  Owner                    = {postgres},
  Publisher                = {IEEE},
  Timestamp                = {2016.02.29}
}

@Article{Candes2008a,
  Title                    = {An Introduction To Compressive Sampling},
  Author                   = {Candes, E.J. and Wakin, M.B.},
  Journal                  = {Signal Processing Magazine, IEEE},
  Year                     = {2008},

  Month                    = {march },
  Number                   = {2},
  Pages                    = {21 -30},
  Volume                   = {25},

  Abstract                 = {Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.},
  Doi                      = {10.1109/MSP.2007.914731},
  File                     = {Candes2008a.pdf:Candes2008a.pdf:PDF},
  ISSN                     = {1053-5888},
  Keywords                 = {Relatively few wavelet;compressed sensing;compressive sampling;data acquisition;image recovery;sampling paradigm;sensing paradigm;signal recovery;data acquisition;image processing;signal processing equipment;signal sampling;},
  Owner                    = {postgres},
  Timestamp                = {2012.08.18}
}

@Article{chen2012design,
  Title                    = {Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors},
  Author                   = {Chen, F. and Chandrakasan, A.P. and Stojanovic, V.M.},
  Journal                  = {Solid-State Circuits, IEEE Journal of},
  Year                     = {2012},
  Number                   = {3},
  Pages                    = {744--756},
  Volume                   = {47},

  __markedentry            = {[postgres:]},
  File                     = {chen2012design.pdf:chen2012design.pdf:PDF},
  Owner                    = {postgres},
  Publisher                = {IEEE},
  Timestamp                = {2016.02.29}
}

@Article{Craven2014,
  Title                    = {Compressed Sensing for Bioelectric Signals: A Review},
  Author                   = {Craven, D. and McGinley, B. and Kilmartin, L. and Glavin, M. and Jones, E.},
  Journal                  = {Biomedical and Health Informatics, IEEE Journal of},
  Year                     = {2014},
  Number                   = {99},
  Pages                    = {1-1},
  Volume                   = {PP},

  __markedentry            = {[postgres:]},
  Doi                      = {10.1109/JBHI.2014.2327194},
  File                     = {Craven2014.pdf:Craven2014.pdf:PDF},
  ISSN                     = {2168-2194},
  Owner                    = {postgres},
  Timestamp                = {2016.02.29}
}

@Article{Gray1998,
  Title                    = {Quantization},
  Author                   = {Robert M. Gray and David L. Neuhoff},
  Journal                  = {IEEE Trans. Info. Theory},
  Year                     = {1998},
  Number                   = {6},
  Pages                    = {1--63},
  Volume                   = {44},

  __markedentry            = {[postgres:]},
  File                     = {Gray1998.pdf:Gray1998.pdf:PDF},
  Owner                    = {postgres},
  Timestamp                = {2015.01.06}
}

@Article{haboba2012pragmatic,
  Title                    = {A Pragmatic Look at Some Compressive Sensing Architectures With Saturation and Quantization},
  Author                   = {Haboba, J. and Mangia, M. and Pareschi, F. and Rovatti, R. and Setti, G.},
  Journal                  = {Emerging and Selected Topics in Circuits and Systems, IEEE Journal on},
  Year                     = {2012},
  Number                   = {3},
  Pages                    = {443--459},
  Volume                   = {2},

  __markedentry            = {[postgres:]},
  File                     = {haboba2012pragmatic.pdf:haboba2012pragmatic.pdf:PDF},
  Owner                    = {postgres},
  Publisher                = {IEEE},
  Timestamp                = {2016.04.01}
}

@InProceedings{Jacques2013,
  Title                    = {Quantized Iterative Hard Thresholding: Bridging 1-bit and High-Resolution Quantized Compressed Sensing},
  Author                   = {Jacques, Laurent and Degraux, Kevin and De Vleeschouwer, Christophe and others},
  Booktitle                = {SAMPTA 2013},
  Year                     = {2013},

  __markedentry            = {[postgres:]},
  Owner                    = {postgres},
  Timestamp                = {2016.02.29}
}

@Article{jacques2011dequantizing,
  Title                    = {Dequantizing compressed sensing: When oversampling and non-gaussian constraints combine},
  Author                   = {Jacques, Laurent and Hammond, David K and Fadili, Mohamed-Jalal},
  Journal                  = {IEEE Transactions on Information Theory},
  Year                     = {2011},
  Number                   = {1},
  Pages                    = {559--571},
  Volume                   = {57},

  __markedentry            = {[postgres:]},
  File                     = {jacques2011dequantizing.pdf:jacques2011dequantizing.pdf:PDF},
  Owner                    = {postgres},
  Publisher                = {Institute of Electrical and Electronics Engineers, Inc., 345 E. 47 th St. NY NY 10017-2394 United States},
  Timestamp                = {2016.02.29}
}

@Article{laska2011democracy,
  Title                    = {Democracy in action: Quantization, saturation, and compressive sensing},
  Author                   = {Laska, J.N. and Boufounos, P.T. and Davenport, M.A. and Baraniuk, R.G.},
  Journal                  = {Applied and Computational Harmonic Analysis},
  Year                     = {2011},
  Number                   = {3},
  Pages                    = {429--443},
  Volume                   = {31},

  __markedentry            = {[postgres:]},
  File                     = {laska2011democracy.pdf:laska2011democracy.pdf:PDF},
  Owner                    = {postgres},
  Publisher                = {Elsevier},
  Timestamp                = {2016.02.29}
}

@InProceedings{liu2013compression,
  Title                    = {Compression via Compressive Sensing: A Low-Power Framework for the Telemonitoring of Multi-Channel Physiological Signals},
  Author                   = {Benyuan Liu and Zhilin Zhang and Hongqi Fan and Qiang Fu},
  Booktitle                = {2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  Year                     = {2013},
  Organization             = {IEEE},
  Pages                    = {9--12},

  Owner                    = {postgres},
  Timestamp                = {2014.03.27}
}

@Article{liu2013energy,
  Title                    = {Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation},
  Author                   = {Liu, Benyuan and Zhang, Zhilin and Xu, Gary and Fan, Hongqi and Fu, Qiang},
  Journal                  = {Biomedical Signal Processing and Control},
  Year                     = {2014},
  Pages                    = {80--88},
  Volume                   = {11C},

  Keywords                 = {Epileptic},
  Owner                    = {postgres},
  Timestamp                = {2013.10.10}
}

@Article{mamaghanian2011compressed,
  Title                    = {Compressed sensing for real-time energy-efficient {ECG} compression on wireless body sensor nodes},
  Author                   = {Mamaghanian, H. and Khaled, N. and Atienza, D. and Vandergheynst, P.},
  Journal                  = {Biomedical Engineering, IEEE Transactions on},
  Year                     = {2011},
  Number                   = {9},
  Pages                    = {2456--2466},
  Volume                   = {58},

  File                     = {mamaghanian2011compressed.pdf:mamaghanian2011compressed.pdf:PDF},
  Owner                    = {liu benyuan},
  Publisher                = {IEEE},
  Timestamp                = {2013.01.21}
}

@Article{meier2013ehealth,
  Title                    = {eHealth: Extending, Enhancing, and Evolving Health Care},
  Author                   = {Meier, Carlos A and Fitzgerald, Maria C and Smith, Joseph M},
  Journal                  = {Annual review of biomedical engineering},
  Year                     = {2013},
  Pages                    = {359--382},
  Volume                   = {15},

  File                     = {meier2013ehealth.pdf:meier2013ehealth.pdf:PDF},
  Owner                    = {postgres},
  Publisher                = {Annual Reviews},
  Timestamp                = {2016.02.29}
}

@InProceedings{Prasad2014,
  Title                    = {Nested sparse Bayesian learning for block-sparse signals with intra-block correlation},
  Author                   = {Prasad, Ranjitha and Murthy, Chandra R and Rao, Bhaskar D},
  Booktitle                = {Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on},
  Year                     = {2014},
  Organization             = {IEEE},
  Pages                    = {7183--7187},

  __markedentry            = {[postgres:]},
  File                     = {Prasad2014.pdf:Prasad2014.pdf:PDF},
  Owner                    = {postgres},
  Timestamp                = {2016.02.29}
}

@Article{Strang1999,
  Title                    = {The discrete cosine transform},
  Author                   = {Strang, Gilbert},
  Journal                  = {SIAM review},
  Year                     = {1999},
  Number                   = {1},
  Pages                    = {135--147},
  Volume                   = {41},

  __markedentry            = {[postgres:]},
  File                     = {Strang1999.pdf:Strang1999.pdf:PDF;Strang1999.pdf:99_static/dl_group/Strang1999.pdf:PDF},
  Owner                    = {postgres},
  Publisher                = {SIAM},
  Timestamp                = {2016.02.29}
}

@InProceedings{wang2015adaptive,
  Title                    = {Adaptive compressed sensing architecture in wireless brain-computer interface},
  Author                   = {Wang, Aosen and Jin, Zhanpeng and Song, Chen and Xu, Wenyao},
  Booktitle                = {Proceedings of the 52nd Annual Design Automation Conference},
  Year                     = {2015},
  Organization             = {ACM},
  Pages                    = {173},

  Owner                    = {postgres},
  Timestamp                = {2016.02.29}
}

@Article{Wang2015,
  Title                    = {Quantization Effects in an Analog-to-Information Front-end in EEG Tele-Monitoring},
  Author                   = {Wang, A. and Xu, W. and Jin, Z. and Gong, F.},
  Journal                  = {Circuits and Systems II: Express Briefs, IEEE Transactions on},
  Year                     = {2015},
  Number                   = {99},
  Pages                    = {1-1},
  Volume                   = {PP},

  __markedentry            = {[postgres:]},
  Doi                      = {10.1109/TCSII.2014.2387677},
  File                     = {Wang2015.pdf:Wang2015.pdf:PDF},
  ISSN                     = {1549-7747},
  Keywords                 = {Compressed sensing;Electroencephalography;Energy consumption;Energy resolution;Quantization (signal);Signal resolution;Wireless communication;EEG Tele-monitoring;Optimal bit resolution;Quantized Compressed Sensing},
  Owner                    = {postgres},
  Timestamp                = {2016.02.29}
}

@Article{Wang2009,
  Title                    = {Mean Squared Error : Love it or leave it ? a new look at signal fidelity measures.},
  Author                   = {Z. Wang and A. Bovik},
  Journal                  = {IEEE Signal Processing Magazine},
  Year                     = {2009},
  Pages                    = {98--117},
  Volume                   = {26 (1)},

  Owner                    = {postgres},
  Timestamp                = {2013.03.17}
}

@Article{Yang2013b,
  Title                    = {Variational Bayesian Algorithm for Quantized Compressed Sensing},
  Author                   = {Zai Yang and Lihua Xie and Cishen Zhang},
  Journal                  = {Signal Processing, IEEE Transactions on},
  Year                     = {2013},

  Month                    = {June},
  Number                   = {11},
  Pages                    = {2815-2824},
  Volume                   = {61},

  __markedentry            = {[postgres:]},
  Doi                      = {10.1109/TSP.2013.2256901},
  File                     = {Yang2013b.pdf:Yang2013b.pdf:PDF},
  ISSN                     = {1053-587X},
  Keywords                 = {Bayes methods;belief networks;compressed sensing;noise measurement;quantisation (signal);1-bit CS processing;CS algorithms;additive noise;digital quantization;high dimensional signals;low dimensional linear measurements;measurement data;measurement noise;multibit CS processing;noiseless environment;noisy environment;quantization error decoupling;quantized compressed sensing;sparsity prior;unsaturated quantizer;variational Bayesian algorithm;variational Bayesian inference based CS algorithm;1-bit compressed sensing;quantized compressed sensing;sparse Bayesian learning;unified framework;variational message passing},
  Owner                    = {postgres},
  Timestamp                = {2016.02.29}
}

@Article{Zhang_TBME2012a,
  Title                    = {Compressed Sensing of {EEG} for Wireless Telemonitoring With Low Energy Consumption and Inexpensive Hardware},
  Author                   = {Zhilin Zhang and Tzyy-Ping Jung and Makeig, S. and Rao, B.D.},
  Journal                  = {Biomedical Engineering, IEEE Transactions on},
  Year                     = {2013},
  Number                   = {1},
  Pages                    = {221-224},
  Volume                   = {60},

  Abstract                 = {Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, although effective in data compression, consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints. However, EEG is nonsparse in the time domain and also nonsparse in transformed domains (such as the wavelet domain). Therefore, it is extremely difficult for current CS algorithms to recover EEG with the quality that satisfies the requirements of clinical diagnosis and engineering applications. Recently, block sparse Bayesian learning (BSBL) was proposed as a new method to the CS problem. This study introduces the technique to the telemonitoring of EEG. Experimental results show that its recovery quality is better than state-of-the-art CS algorithms, and sufficient for practical use. These results suggest that BSBL is very promising for telemonitoring of EEG and other nonsparse physiological signals.},
  Doi                      = {10.1109/TBME.2012.2217959},
  File                     = {Zhang_TBME2012a.pdf:Zhang_TBME2012a.pdf:PDF},
  ISSN                     = {0018-9294},
  Keywords                 = {Bayes methods;body area networks;compressed sensing;learning (artificial intelligence);medical signal processing;patient monitoring;telemedicine;wireless sensor networks;BSBL;EEG compressed sensing;block sparse Bayesian learning;data compression methodologies;device cost;electroencephalogram;energy consumption;nonsparse physiological signals;nonsparse time domain EEG data;nonsparse wavelet domain EEG data;personalized medicine;wireless body area networks;wireless telemonitoring;Compressed sensing;Dictionaries;Electroencephalography;Energy consumption;Sensors;Sparse matrices;Wavelet transforms;Block sparse Bayesian learning (BSBL);compressed sensing (CS);electroencephalogram (EEG);healthcare;telemonitoring;wireless body-area network (WBAN)},
  Owner                    = {postgres},
  Timestamp                = {2016.02.29}
}

@Article{Zhang_TBME2012b,
  Title                    = {Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal {ECG} Via Block Sparse Bayesian Learning},
  Author                   = {ZhiLin Zhang and Tzyy-Ping Jung and Makeig, S. and Rao, B.D.},
  Journal                  = {Biomedical Engineering, IEEE Transactions on},
  Year                     = {2013},
  Number                   = {2},
  Pages                    = {300-309},
  Volume                   = {60},

  Doi                      = {10.1109/TBME.2012.2226175},
  File                     = {Zhang_TBME2012b.pdf:Zhang_TBME2012b.pdf:PDF},
  ISSN                     = {0018-9294},
  Keywords                 = {Bayes methods;body area networks;compressed sensing;data compression;electrocardiography;energy consumption;independent component analysis;medical signal processing;signal denoising;signal reconstruction;telemedicine;CPU;block sparse Bayesian learning;current compressed sensing algorithms;data compressing-reconstructing;energy-efficient wireless telemonitoring;independent component analysis decomposition;low energy consumption;multichannel recordings;noise contamination;noninvasive fetal ECG;sparse binary sensing matrix;telemedicine;wavelet algorithms;wireless body area network;Correlation;Electrocardiography;Noise;Partitioning algorithms;Sensors;Signal processing algorithms;Sparse matrices;Block sparse Bayesian learning (BSBL);compressed sensing (CS);fetal ECG (FECG);healthcare;independent component analysis (ICA);telemedicine;telemonitoring},
  Owner                    = {postgres},
  Timestamp                = {2016.02.29}
}

@Article{Zhang2015,
  Title                    = {{TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise}},
  Author                   = {Zhilin Zhang and Zhuoyue Pi and Benyuan Liu},
  Journal                  = {IEEE Transactions on Biomedical Engineering},
  Year                     = {2015},
  Number                   = {2},
  Pages                    = {522 -- 531},
  Volume                   = {62},

  File                     = {Zhang2015.pdf:Zhang2015.pdf:PDF},
  Owner                    = {postgres},
  Timestamp                = {2014.09.09}
}

@Article{Zhang2012a,
  Title                    = {Extension of {SBL} Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation},
  Author                   = {Zhang, Z. and Rao, B.D.},
  Journal                  = {Signal Processing, IEEE Transactions on},
  Year                     = {2013},
  Number                   = {8},
  Pages                    = {2009-2015},
  Volume                   = {61},

  Doi                      = {10.1109/TSP.2013.2241055},
  File                     = {Zhang2012a.pdf:Zhang2012a.pdf:PDF},
  ISSN                     = {1053-587X},
  Keywords                 = {Bayesian methods;Bismuth;Correlation;Cost function;Partitioning algorithms;Sparse matrices;Vectors;Block sparse model;compressed sensing;intra-block correlation;sparse Bayesian learning (SBL);sparse signal recovery},
  Owner                    = {postgres},
  Timestamp                = {2016.02.29}
}

@Article{Zhang2011,
  Title                    = {Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning},
  Author                   = {Zhilin Zhang and Bhaskar D. Rao},
  Journal                  = {IEEE Journal of Selected Topics in Signal Processing},
  Year                     = {2011},
  Number                   = {5},
  Pages                    = {912--926},
  Volume                   = {5},

  File                     = {Zhang2011.pdf:Zhang2011.pdf:PDF},
  Owner                    = {postgres},
  Timestamp                = {2012.08.10}
}

@Conference{Zhang_Asilomar,
  Title                    = {Compressed sensing for energy-efficient wireless telemonitoring: Challenges and opportunities},
  Author                   = {Zhilin Zhang and Bhaskar D Rao and Tzyy-Ping Jung},
  Booktitle                = {Asilomar Conference on Signals, Systems and Computers (Asilomar 2013)},
  Year                     = {2013},

  Owner                    = {postgres},
  Timestamp                = {2013.11.16}
}

@InProceedings{zhou2011multi,
  Title                    = {A multi-task learning formulation for predicting disease progression},
  Author                   = {Zhou, J. and Yuan, L. and Liu, J. and Ye, J.},
  Booktitle                = {Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining},
  Year                     = {2011},
  Organization             = {ACM},
  Pages                    = {814--822},

  File                     = {zhou2011multi.pdf:zhou2011multi.pdf:PDF},
  Owner                    = {postgres},
  Timestamp                = {2016.02.29}
}

@Article{Zymnis2010,
  Title                    = {{Compressed Sensing With Quantized Measurements}},
  Author                   = {Argyrios Zymnis and Stephen Boyd and Emmanuel Candes},
  Journal                  = {IEEE Transaction on Signal Processing},
  Year                     = {2010},
  Number                   = {2},
  Pages                    = {149--152},
  Volume                   = {17},

  __markedentry            = {[postgres:]},
  File                     = {Zymnis2010.pdf:Zymnis2010.pdf:PDF},
  Owner                    = {postgres},
  Timestamp                = {2015.01.06}
}

